class_docstr: Apply gaussian blur given tensor image or a batch of tensor images randomly. The standard deviation is
  sampled for each instance.
  
  .. image:: _static/img/RandomGaussianBlur.png
  
  Args:
      kernel_size: the size of the kernel.
      sigma: the range for the standard deviation of the kernel.
      border_type: the padding mode to be applied before convolving.
        The expected modes are: ``constant``, ``reflect``, ``replicate`` or ``circular``.
      separable: run as composition of two 1d-convolutions.
      same_on_batch: apply the same transformation across the batch.
      p: probability of applying the transformation.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
      silence_instantiation_warning: if True, silence the warning at instantiation.
  
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.filters.gaussian_blur2d`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> input = torch.rand(1, 1, 5, 5)
      >>> blur = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.)
      >>> blur(input)
      tensor([[[[0.5941, 0.5833, 0.5022, 0.4384, 0.3934],
                [0.5310, 0.4964, 0.4113, 0.3637, 0.3472],
                [0.4991, 0.4997, 0.4312, 0.3620, 0.3081],
                [0.6082, 0.5667, 0.4954, 0.3825, 0.3508],
                [0.7042, 0.6849, 0.6275, 0.4753, 0.4105]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      kernel_size: Union[Tuple[int, int], int],
      sigma: Union[Tuple[float, float], Tensor],
      border_type: str = "reflect",
      separable: bool = True,
      same_on_batch: bool = False,
      p: float = 0.5,
      keepdim: bool = False,
      silence_instantiation_warning: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim
      )
  
      if not silence_instantiation_warning:
          warnings.warn(
              "`RandomGaussianBlur` has changed its behavior and now randomly sample sigma for both axes. "
              "To retrieve old behavior please consider using kornia.filters.GaussianBlur2d",
              category=DeprecationWarning,
          )
  
      self.flags = {
          "kernel_size": kernel_size,
          "separable": separable,
          "border_type": BorderType.get(border_type),
      }
      self._param_generator = rg.RandomGaussianBlurGenerator(sigma)
  
file_path: kornia/kornia/augmentation/_2d/intensity/gaussian_blur.py
imports: import warnings
  from typing import Any, Dict, Optional, Tuple, Union
  from torch import Tensor
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.constants import BorderType
  from kornia.filters import gaussian_blur2d
label_desc: Use image node_id 1 to create a new RandomGaussianBlur object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomGaussianBlur(kernel_size, sigma, border_type, separable, same_on_batch, p, keepdim, silence_instantiation_warning)